Overview

Dataset statistics

Number of variables18
Number of observations515431
Missing cells6536
Missing cells (%)0.1%
Duplicate rows29
Duplicate rows (%)< 0.1%
Total size in memory70.8 MiB
Average record size in memory144.0 B

Variable types

Text7
Numeric9
DateTime1
Categorical1

Alerts

Dataset has 29 (< 0.1%) duplicate rowsDuplicates
additional_number_of_scoring is highly overall correlated with total_number_of_reviewsHigh correlation
total_number_of_reviews is highly overall correlated with additional_number_of_scoringHigh correlation
reviewer_score is highly overall correlated with sampleHigh correlation
sample is highly overall correlated with reviewer_scoreHigh correlation
review_total_negative_word_counts has 127816 (24.8%) zerosZeros
review_total_positive_word_counts has 35924 (7.0%) zerosZeros
reviewer_score has 128935 (25.0%) zerosZeros

Reproduction

Analysis started2023-11-09 18:28:23.590288
Analysis finished2023-11-09 18:29:21.804067
Duration58.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1493
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:22.133829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length96
Median length78
Mean length59.880743
Min length34

Characters and Unicode

Total characters30864391
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVia Senigallia 6 20161 Milan Italy
2nd rowArlandaweg 10 Westpoort 1043 EW Amsterdam Netherlands
3rd rowMallorca 251 Eixample 08008 Barcelona Spain
4th rowPiazza Della Repubblica 17 Central Station 20124 Milan Italy
5th rowSingel 303 309 Amsterdam City Center 1012 WJ Amsterdam Netherlands
ValueCountFrequency (%)
london 283656
 
5.7%
kingdom 262691
 
5.3%
united 262300
 
5.2%
westminster 95105
 
1.9%
borough 90619
 
1.8%
amsterdam 82632
 
1.7%
city 62594
 
1.3%
barcelona 61231
 
1.2%
street 60433
 
1.2%
spain 60149
 
1.2%
Other values (2525) 3681626
73.6%
2023-11-09T21:29:22.712433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4488010
 
14.5%
n 2380505
 
7.7%
e 2223708
 
7.2%
a 1874958
 
6.1%
o 1633164
 
5.3%
t 1599765
 
5.2%
r 1480166
 
4.8%
i 1459005
 
4.7%
d 1357818
 
4.4%
s 939373
 
3.0%
Other values (53) 11427919
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19001876
61.6%
Space Separator 4488010
 
14.5%
Uppercase Letter 4383600
 
14.2%
Decimal Number 2990905
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2380505
12.5%
e 2223708
11.7%
a 1874958
9.9%
o 1633164
8.6%
t 1599765
8.4%
r 1480166
7.8%
i 1459005
7.7%
d 1357818
7.1%
s 939373
 
4.9%
m 741137
 
3.9%
Other values (16) 3312277
17.4%
Uppercase Letter
ValueCountFrequency (%)
L 401274
 
9.2%
S 398881
 
9.1%
W 363630
 
8.3%
C 343437
 
7.8%
K 340269
 
7.8%
U 295947
 
6.8%
B 283345
 
6.5%
A 243318
 
5.6%
P 202834
 
4.6%
E 167216
 
3.8%
Other values (16) 1343449
30.6%
Decimal Number
ValueCountFrequency (%)
1 725572
24.3%
0 587741
19.7%
2 361242
12.1%
5 236677
 
7.9%
7 223113
 
7.5%
3 211488
 
7.1%
8 202229
 
6.8%
4 180522
 
6.0%
6 144235
 
4.8%
9 118086
 
3.9%
Space Separator
ValueCountFrequency (%)
4488010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23385476
75.8%
Common 7478915
 
24.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2380505
 
10.2%
e 2223708
 
9.5%
a 1874958
 
8.0%
o 1633164
 
7.0%
t 1599765
 
6.8%
r 1480166
 
6.3%
i 1459005
 
6.2%
d 1357818
 
5.8%
s 939373
 
4.0%
m 741137
 
3.2%
Other values (42) 7695877
32.9%
Common
ValueCountFrequency (%)
4488010
60.0%
1 725572
 
9.7%
0 587741
 
7.9%
2 361242
 
4.8%
5 236677
 
3.2%
7 223113
 
3.0%
3 211488
 
2.8%
8 202229
 
2.7%
4 180522
 
2.4%
6 144235
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30864391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4488010
 
14.5%
n 2380505
 
7.7%
e 2223708
 
7.2%
a 1874958
 
6.1%
o 1633164
 
5.3%
t 1599765
 
5.2%
r 1480166
 
4.8%
i 1459005
 
4.7%
d 1357818
 
4.4%
s 939373
 
3.0%
Other values (53) 11427919
37.0%

additional_number_of_scoring
Real number (ℝ)

HIGH CORRELATION 

Distinct480
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.27508
Minimum1
Maximum2682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:22.918036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58
Q1169
median342
Q3660
95-th percentile1444
Maximum2682
Range2681
Interquartile range (IQR)491

Descriptive statistics

Standard deviation500.61896
Coefficient of variation (CV)1.004704
Kurtosis5.747933
Mean498.27508
Median Absolute Deviation (MAD)202
Skewness2.2070482
Sum2.5682642 × 108
Variance250619.34
MonotonicityNot monotonic
2023-11-09T21:29:23.118318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2682 4789
 
0.9%
2288 4256
 
0.8%
2623 4169
 
0.8%
1831 3578
 
0.7%
1936 3212
 
0.6%
256 3079
 
0.6%
1274 2958
 
0.6%
832 2934
 
0.6%
211 2858
 
0.6%
404 2836
 
0.6%
Other values (470) 480762
93.3%
ValueCountFrequency (%)
1 13
 
< 0.1%
4 12
 
< 0.1%
5 39
 
< 0.1%
6 118
< 0.1%
7 56
 
< 0.1%
8 57
 
< 0.1%
9 89
< 0.1%
10 195
< 0.1%
11 143
< 0.1%
12 67
 
< 0.1%
ValueCountFrequency (%)
2682 4789
0.9%
2623 4169
0.8%
2288 4256
0.8%
1936 3212
0.6%
1831 3578
0.7%
1485 2628
0.5%
1471 2155
0.4%
1444 2565
0.5%
1427 2227
0.4%
1322 2223
0.4%
Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
Minimum2015-08-04 00:00:00
Maximum2017-08-03 00:00:00
2023-11-09T21:29:23.317479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:23.517934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

average_score
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3976296
Minimum5.2
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:23.729056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile7.4
Q18.1
median8.4
Q38.8
95-th percentile9.2
Maximum9.8
Range4.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.54803327
Coefficient of variation (CV)0.065260472
Kurtosis0.4237615
Mean8.3976296
Median Absolute Deviation (MAD)0.3
Skewness-0.54586802
Sum4328398.6
Variance0.30034046
MonotonicityNot monotonic
2023-11-09T21:29:23.919416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
8.4 41222
 
8.0%
8.1 38120
 
7.4%
8.5 38066
 
7.4%
8.7 37798
 
7.3%
8.6 36945
 
7.2%
8.2 34847
 
6.8%
8.3 32880
 
6.4%
8.8 30836
 
6.0%
8.9 28487
 
5.5%
8 22341
 
4.3%
Other values (24) 173889
33.7%
ValueCountFrequency (%)
5.2 65
 
< 0.1%
6.4 1163
 
0.2%
6.6 400
 
0.1%
6.7 965
 
0.2%
6.8 1327
 
0.3%
6.9 1737
 
0.3%
7 3899
0.8%
7.1 6780
1.3%
7.2 684
 
0.1%
7.3 3997
0.8%
ValueCountFrequency (%)
9.8 28
 
< 0.1%
9.6 915
 
0.2%
9.5 1207
 
0.2%
9.4 9339
 
1.8%
9.3 12659
2.5%
9.2 12935
2.5%
9.1 21379
4.1%
9 21001
4.1%
8.9 28487
5.5%
8.8 30836
6.0%
Distinct1492
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:24.170607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length43
Mean length25.308113
Min length2

Characters and Unicode

Total characters13044586
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel Da Vinci
2nd rowUrban Lodge Hotel
3rd rowAlexandra Barcelona A DoubleTree by Hilton
4th rowHotel Principe Di Savoia
5th rowHotel Esther a
ValueCountFrequency (%)
hotel 234949
 
11.6%
london 137227
 
6.8%
the 58688
 
2.9%
park 43928
 
2.2%
amsterdam 39866
 
2.0%
hilton 35490
 
1.8%
by 26928
 
1.3%
plaza 23104
 
1.1%
paris 21791
 
1.1%
grand 18430
 
0.9%
Other values (1595) 1385484
68.4%
2023-11-09T21:29:24.685112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1512309
 
11.6%
e 1228451
 
9.4%
o 1084419
 
8.3%
n 970216
 
7.4%
a 903501
 
6.9%
t 815894
 
6.3%
l 767890
 
5.9%
r 726361
 
5.6%
i 570486
 
4.4%
s 434786
 
3.3%
Other values (53) 4030273
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9510919
72.9%
Uppercase Letter 1986425
 
15.2%
Space Separator 1512309
 
11.6%
Decimal Number 34933
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1228451
12.9%
o 1084419
11.4%
n 970216
10.2%
a 903501
9.5%
t 815894
8.6%
l 767890
8.1%
r 726361
7.6%
i 570486
 
6.0%
s 434786
 
4.6%
d 417172
 
4.4%
Other values (16) 1591743
16.7%
Uppercase Letter
ValueCountFrequency (%)
H 366316
18.4%
L 178460
 
9.0%
P 162962
 
8.2%
C 138637
 
7.0%
T 128310
 
6.5%
A 122329
 
6.2%
S 117547
 
5.9%
M 112334
 
5.7%
B 109084
 
5.5%
G 77517
 
3.9%
Other values (16) 472929
23.8%
Decimal Number
ValueCountFrequency (%)
4 7293
20.9%
1 6887
19.7%
2 5391
15.4%
0 4773
13.7%
8 4021
11.5%
5 1905
 
5.5%
7 1413
 
4.0%
9 1276
 
3.7%
6 1154
 
3.3%
3 820
 
2.3%
Space Separator
ValueCountFrequency (%)
1512309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11497344
88.1%
Common 1547242
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1228451
 
10.7%
o 1084419
 
9.4%
n 970216
 
8.4%
a 903501
 
7.9%
t 815894
 
7.1%
l 767890
 
6.7%
r 726361
 
6.3%
i 570486
 
5.0%
s 434786
 
3.8%
d 417172
 
3.6%
Other values (42) 3578168
31.1%
Common
ValueCountFrequency (%)
1512309
97.7%
4 7293
 
0.5%
1 6887
 
0.4%
2 5391
 
0.3%
0 4773
 
0.3%
8 4021
 
0.3%
5 1905
 
0.1%
7 1413
 
0.1%
9 1276
 
0.1%
6 1154
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13044586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1512309
 
11.6%
e 1228451
 
9.4%
o 1084419
 
8.3%
n 970216
 
7.4%
a 903501
 
6.9%
t 815894
 
6.3%
l 767890
 
5.9%
r 726361
 
5.6%
i 570486
 
4.4%
s 434786
 
3.3%
Other values (53) 4030273
30.9%
Distinct227
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:25.002383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length14.036255
Min length1

Characters and Unicode

Total characters7234721
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row United Kingdom
2nd row Belgium
3rd row Sweden
4th row United States of America
5th row United Kingdom
ValueCountFrequency (%)
united 290859
31.9%
kingdom 245165
26.9%
of 35804
 
3.9%
states 35464
 
3.9%
america 35390
 
3.9%
australia 21663
 
2.4%
ireland 14820
 
1.6%
arab 10230
 
1.1%
emirates 10230
 
1.1%
saudi 8945
 
1.0%
Other values (262) 203752
22.3%
2023-11-09T21:29:25.506045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1427753
19.7%
i 707320
9.8%
n 664281
9.2%
d 607023
 
8.4%
e 500464
 
6.9%
t 445275
 
6.2%
a 370487
 
5.1%
o 323560
 
4.5%
m 315098
 
4.4%
U 292136
 
4.0%
Other values (42) 1581324
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4930916
68.2%
Space Separator 1427753
 
19.7%
Uppercase Letter 876052
 
12.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 707320
14.3%
n 664281
13.5%
d 607023
12.3%
e 500464
10.1%
t 445275
9.0%
a 370487
7.5%
o 323560
6.6%
m 315098
6.4%
g 272651
 
5.5%
r 191548
 
3.9%
Other values (16) 533209
10.8%
Uppercase Letter
ValueCountFrequency (%)
U 292136
33.3%
K 254825
29.1%
A 84112
 
9.6%
S 72530
 
8.3%
I 35439
 
4.0%
C 17243
 
2.0%
N 15489
 
1.8%
E 13498
 
1.5%
G 12967
 
1.5%
B 11800
 
1.3%
Other values (15) 66013
 
7.5%
Space Separator
ValueCountFrequency (%)
1427753
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5806968
80.3%
Common 1427753
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 707320
12.2%
n 664281
11.4%
d 607023
10.5%
e 500464
 
8.6%
t 445275
 
7.7%
a 370487
 
6.4%
o 323560
 
5.6%
m 315098
 
5.4%
U 292136
 
5.0%
g 272651
 
4.7%
Other values (41) 1308673
22.5%
Common
ValueCountFrequency (%)
1427753
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7234721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1427753
19.7%
i 707320
9.8%
n 664281
9.2%
d 607023
 
8.4%
e 500464
 
6.9%
t 445275
 
6.2%
a 370487
 
5.1%
o 323560
 
4.5%
m 315098
 
4.4%
U 292136
 
4.0%
Other values (42) 1581324
21.9%
Distinct330011
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:26.023793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1966
Median length1910
Mean length93.799847
Min length1

Characters and Unicode

Total characters48347349
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323747 ?
Unique (%)62.8%

Sample

1st row Would have appreciated a shop in the hotel that sold drinking water etc but not necessity Would recommend if like us you arrive late at night to bring drinks from plane airport as there s no shop nearby There is a minibar though if you want to pay those prices
2nd row No tissue paper box was present at the room
3rd row Pillows
4th rowNo Negative
5th rowNo Negative
ValueCountFrequency (%)
the 531014
 
5.8%
was 236629
 
2.6%
a 230131
 
2.5%
to 228748
 
2.5%
and 219362
 
2.4%
no 197739
 
2.1%
room 175898
 
1.9%
in 167932
 
1.8%
negative 129371
 
1.4%
not 125608
 
1.4%
Other values (55627) 6957269
75.6%
2023-11-09T21:29:26.626163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9296160
19.2%
e 4707415
 
9.7%
t 3566208
 
7.4%
o 3561163
 
7.4%
a 3075708
 
6.4%
i 2440915
 
5.0%
n 2398199
 
5.0%
r 2320280
 
4.8%
s 2084557
 
4.3%
h 1783020
 
3.7%
Other values (53) 13113724
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37604485
77.8%
Space Separator 9296160
 
19.2%
Uppercase Letter 1249538
 
2.6%
Decimal Number 197166
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4707415
12.5%
t 3566208
 
9.5%
o 3561163
 
9.5%
a 3075708
 
8.2%
i 2440915
 
6.5%
n 2398199
 
6.4%
r 2320280
 
6.2%
s 2084557
 
5.5%
h 1783020
 
4.7%
l 1573945
 
4.2%
Other values (16) 10093075
26.8%
Uppercase Letter
ValueCountFrequency (%)
N 345285
27.6%
T 173927
13.9%
I 152902
12.2%
W 60039
 
4.8%
A 58045
 
4.6%
S 54561
 
4.4%
B 53980
 
4.3%
R 46249
 
3.7%
O 34350
 
2.7%
E 33480
 
2.7%
Other values (16) 236720
18.9%
Decimal Number
ValueCountFrequency (%)
0 40979
20.8%
1 35807
18.2%
2 32771
16.6%
5 22588
11.5%
3 21838
11.1%
4 18870
9.6%
6 6931
 
3.5%
8 6116
 
3.1%
7 6066
 
3.1%
9 5200
 
2.6%
Space Separator
ValueCountFrequency (%)
9296160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38854023
80.4%
Common 9493326
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4707415
12.1%
t 3566208
 
9.2%
o 3561163
 
9.2%
a 3075708
 
7.9%
i 2440915
 
6.3%
n 2398199
 
6.2%
r 2320280
 
6.0%
s 2084557
 
5.4%
h 1783020
 
4.6%
l 1573945
 
4.1%
Other values (42) 11342613
29.2%
Common
ValueCountFrequency (%)
9296160
97.9%
0 40979
 
0.4%
1 35807
 
0.4%
2 32771
 
0.3%
5 22588
 
0.2%
3 21838
 
0.2%
4 18870
 
0.2%
6 6931
 
0.1%
8 6116
 
0.1%
7 6066
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48347349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9296160
19.2%
e 4707415
 
9.7%
t 3566208
 
7.4%
o 3561163
 
7.4%
a 3075708
 
6.4%
i 2440915
 
5.0%
n 2398199
 
5.0%
r 2320280
 
4.8%
s 2084557
 
4.3%
h 1783020
 
3.7%
Other values (53) 13113724
27.1%

review_total_negative_word_counts
Real number (ℝ)

ZEROS 

Distinct402
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.53976
Minimum0
Maximum408
Zeros127816
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:26.918605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q323
95-th percentile69
Maximum408
Range408
Interquartile range (IQR)21

Descriptive statistics

Standard deviation29.690973
Coefficient of variation (CV)1.6014756
Kurtosis31.420396
Mean18.53976
Median Absolute Deviation (MAD)9
Skewness4.4082792
Sum9555967
Variance881.55389
MonotonicityNot monotonic
2023-11-09T21:29:27.121078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127816
24.8%
2 24639
 
4.8%
3 18134
 
3.5%
6 17735
 
3.4%
5 16794
 
3.3%
7 16128
 
3.1%
4 15054
 
2.9%
8 14711
 
2.9%
9 13632
 
2.6%
10 12414
 
2.4%
Other values (392) 238374
46.2%
ValueCountFrequency (%)
0 127816
24.8%
2 24639
 
4.8%
3 18134
 
3.5%
4 15054
 
2.9%
5 16794
 
3.3%
6 17735
 
3.4%
7 16128
 
3.1%
8 14711
 
2.9%
9 13632
 
2.6%
10 12414
 
2.4%
ValueCountFrequency (%)
408 1
< 0.1%
403 2
< 0.1%
402 2
< 0.1%
401 1
< 0.1%
400 1
< 0.1%
399 2
< 0.1%
398 1
< 0.1%
397 1
< 0.1%
395 1
< 0.1%
393 2
< 0.1%

total_number_of_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct1142
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2744.2591
Minimum43
Maximum16670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:27.322294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile437
Q11161
median2134
Q33633
95-th percentile7371
Maximum16670
Range16627
Interquartile range (IQR)2472

Descriptive statistics

Standard deviation2317.8237
Coefficient of variation (CV)0.8446082
Kurtosis6.4162235
Mean2744.2591
Median Absolute Deviation (MAD)1118
Skewness2.0855871
Sum1.4144762 × 109
Variance5372306.8
MonotonicityNot monotonic
2023-11-09T21:29:27.537726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9086 4789
 
0.9%
9568 4256
 
0.8%
12158 4169
 
0.8%
7105 3578
 
0.7%
7491 3212
 
0.6%
6539 2958
 
0.6%
5945 2768
 
0.5%
6977 2628
 
0.5%
5726 2565
 
0.5%
4204 2551
 
0.5%
Other values (1132) 481957
93.5%
ValueCountFrequency (%)
43 12
 
< 0.1%
45 12
 
< 0.1%
49 40
< 0.1%
51 13
 
< 0.1%
54 13
 
< 0.1%
59 75
< 0.1%
60 23
 
< 0.1%
61 17
 
< 0.1%
64 31
< 0.1%
66 12
 
< 0.1%
ValueCountFrequency (%)
16670 1876
 
0.4%
12158 4169
0.8%
10842 1118
 
0.2%
9568 4256
0.8%
9086 4789
0.9%
8177 1809
 
0.4%
7656 1576
 
0.3%
7586 1686
 
0.3%
7491 3212
0.6%
7371 1335
 
0.3%
Distinct412601
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:27.908149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1960
Median length1841
Mean length94.625868
Min length1

Characters and Unicode

Total characters48773106
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403407 ?
Unique (%)78.3%

Sample

1st row Hotel was great clean friendly staff free breakfast every morning with good selection good wifi connection nice sized room with bath fridge in room Personally loved the fact that the hotel isn t in the city centre but is literally next to a train station that you can easily get to and from the airport city Would definitely stay again
2nd rowNo Positive
3rd row Nice welcoming and service
4th row Everything including the nice upgrade The Hotel has been revamped and what a surprise Love every second of it including in room dining which was excellent
5th row Lovely hotel v welcoming staff
ValueCountFrequency (%)
the 515007
 
6.1%
and 420393
 
5.0%
was 236640
 
2.8%
staff 194472
 
2.3%
location 192730
 
2.3%
very 192630
 
2.3%
to 187828
 
2.2%
a 164868
 
1.9%
room 140673
 
1.7%
hotel 125259
 
1.5%
Other values (51225) 6117076
72.1%
2023-11-09T21:29:28.487236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8719393
17.9%
e 4821025
 
9.9%
a 3495080
 
7.2%
o 3416437
 
7.0%
t 3412549
 
7.0%
n 2491231
 
5.1%
r 2426192
 
5.0%
i 2341102
 
4.8%
s 2115673
 
4.3%
l 2083831
 
4.3%
Other values (53) 13450593
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38579328
79.1%
Space Separator 8719393
 
17.9%
Uppercase Letter 1370445
 
2.8%
Decimal Number 103940
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4821025
12.5%
a 3495080
 
9.1%
o 3416437
 
8.9%
t 3412549
 
8.8%
n 2491231
 
6.5%
r 2426192
 
6.3%
i 2341102
 
6.1%
s 2115673
 
5.5%
l 2083831
 
5.4%
h 1465956
 
3.8%
Other values (16) 10510252
27.2%
Uppercase Letter
ValueCountFrequency (%)
T 213590
15.6%
L 110346
 
8.1%
S 94440
 
6.9%
I 90852
 
6.6%
G 82496
 
6.0%
N 75658
 
5.5%
P 74987
 
5.5%
E 72684
 
5.3%
C 68538
 
5.0%
B 68006
 
5.0%
Other values (16) 418848
30.6%
Decimal Number
ValueCountFrequency (%)
1 22480
21.6%
0 21269
20.5%
2 18460
17.8%
5 15475
14.9%
3 8777
 
8.4%
4 7262
 
7.0%
7 3183
 
3.1%
6 2869
 
2.8%
8 2306
 
2.2%
9 1859
 
1.8%
Space Separator
ValueCountFrequency (%)
8719393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39949773
81.9%
Common 8823333
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4821025
12.1%
a 3495080
 
8.7%
o 3416437
 
8.6%
t 3412549
 
8.5%
n 2491231
 
6.2%
r 2426192
 
6.1%
i 2341102
 
5.9%
s 2115673
 
5.3%
l 2083831
 
5.2%
h 1465956
 
3.7%
Other values (42) 11880697
29.7%
Common
ValueCountFrequency (%)
8719393
98.8%
1 22480
 
0.3%
0 21269
 
0.2%
2 18460
 
0.2%
5 15475
 
0.2%
3 8777
 
0.1%
4 7262
 
0.1%
7 3183
 
< 0.1%
6 2869
 
< 0.1%
8 2306
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48773106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8719393
17.9%
e 4821025
 
9.9%
a 3495080
 
7.2%
o 3416437
 
7.0%
t 3412549
 
7.0%
n 2491231
 
5.1%
r 2426192
 
5.0%
i 2341102
 
4.8%
s 2115673
 
4.3%
l 2083831
 
4.3%
Other values (53) 13450593
27.6%

review_total_positive_word_counts
Real number (ℝ)

ZEROS 

Distinct365
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.777342
Minimum0
Maximum395
Zeros35924
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:28.703009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q322
95-th percentile56
Maximum395
Range395
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.803204
Coefficient of variation (CV)1.2264603
Kurtosis32.947192
Mean17.777342
Median Absolute Deviation (MAD)7
Skewness4.1908652
Sum9162993
Variance475.37971
MonotonicityNot monotonic
2023-11-09T21:29:28.973000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35924
 
7.0%
6 26901
 
5.2%
5 26824
 
5.2%
4 24635
 
4.8%
7 24527
 
4.8%
8 23225
 
4.5%
3 22516
 
4.4%
9 21198
 
4.1%
2 20916
 
4.1%
10 19603
 
3.8%
Other values (355) 269162
52.2%
ValueCountFrequency (%)
0 35924
7.0%
2 20916
4.1%
3 22516
4.4%
4 24635
4.8%
5 26824
5.2%
6 26901
5.2%
7 24527
4.8%
8 23225
4.5%
9 21198
4.1%
10 19603
3.8%
ValueCountFrequency (%)
395 1
< 0.1%
386 1
< 0.1%
384 2
< 0.1%
383 2
< 0.1%
382 1
< 0.1%
380 1
< 0.1%
378 1
< 0.1%
377 1
< 0.1%
375 2
< 0.1%
374 1
< 0.1%
Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1652151
Minimum1
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:29.173028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile26
Maximum355
Range354
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.038742
Coefficient of variation (CV)1.5406016
Kurtosis51.512463
Mean7.1652151
Median Absolute Deviation (MAD)2
Skewness5.0884652
Sum3693174
Variance121.85381
MonotonicityNot monotonic
2023-11-09T21:29:29.357949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154561
30.0%
2 67036
13.0%
3 46818
 
9.1%
4 35008
 
6.8%
5 27614
 
5.4%
6 22604
 
4.4%
7 18606
 
3.6%
8 16139
 
3.1%
9 13532
 
2.6%
10 11708
 
2.3%
Other values (188) 101805
19.8%
ValueCountFrequency (%)
1 154561
30.0%
2 67036
13.0%
3 46818
 
9.1%
4 35008
 
6.8%
5 27614
 
5.4%
6 22604
 
4.4%
7 18606
 
3.6%
8 16139
 
3.1%
9 13532
 
2.6%
10 11708
 
2.3%
ValueCountFrequency (%)
355 1
 
< 0.1%
330 1
 
< 0.1%
315 4
< 0.1%
297 2
< 0.1%
281 2
< 0.1%
270 2
< 0.1%
250 3
< 0.1%
239 1
 
< 0.1%
237 1
 
< 0.1%
232 1
 
< 0.1%

tags
Text

Distinct55242
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:29.666681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length213
Median length178
Mean length102.42023
Min length11

Characters and Unicode

Total characters52790564
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29900 ?
Unique (%)5.8%

Sample

1st row[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']
2nd row[' Leisure trip ', ' Group ', ' Triple Room ', ' Stayed 1 night ']
3rd row[' Business trip ', ' Solo traveler ', ' Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
4th row[' Leisure trip ', ' Couple ', ' Ambassador Junior Suite ', ' Stayed 1 night ']
5th row[' Business trip ', ' Solo traveler ', ' Classic Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']
ValueCountFrequency (%)
4710412
41.1%
stayed 515239
 
4.5%
trip 500422
 
4.4%
room 467154
 
4.1%
leisure 417660
 
3.6%
nights 321679
 
2.8%
a 309190
 
2.7%
from 307793
 
2.7%
mobile 307523
 
2.7%
device 307470
 
2.7%
Other values (644) 3304733
28.8%
2023-11-09T21:29:30.236829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10953844
20.7%
' 4710412
 
8.9%
e 4062648
 
7.7%
i 3221374
 
6.1%
o 2785686
 
5.3%
t 2605233
 
4.9%
r 2079818
 
3.9%
, 1839775
 
3.5%
u 1791582
 
3.4%
m 1538482
 
2.9%
Other values (57) 17201710
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30285178
57.4%
Space Separator 10953844
 
20.7%
Other Punctuation 6550187
 
12.4%
Uppercase Letter 3410924
 
6.5%
Decimal Number 559569
 
1.1%
Close Punctuation 515431
 
1.0%
Open Punctuation 515431
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4062648
13.4%
i 3221374
10.6%
o 2785686
 
9.2%
t 2605233
 
8.6%
r 2079818
 
6.9%
u 1791582
 
5.9%
m 1538482
 
5.1%
l 1500151
 
5.0%
d 1480737
 
4.9%
a 1309899
 
4.3%
Other values (16) 7909568
26.1%
Uppercase Letter
ValueCountFrequency (%)
S 1181372
34.6%
R 470583
 
13.8%
L 426560
 
12.5%
D 374300
 
11.0%
C 323000
 
9.5%
T 156926
 
4.6%
B 112897
 
3.3%
F 99022
 
2.9%
G 83812
 
2.5%
K 37503
 
1.1%
Other values (16) 144949
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 208250
37.2%
2 159142
28.4%
3 98358
17.6%
4 48625
 
8.7%
5 21373
 
3.8%
6 9926
 
1.8%
7 7452
 
1.3%
0 2574
 
0.5%
8 2542
 
0.5%
9 1327
 
0.2%
Other Punctuation
ValueCountFrequency (%)
' 4710412
71.9%
, 1839775
 
28.1%
Space Separator
ValueCountFrequency (%)
10953844
100.0%
Close Punctuation
ValueCountFrequency (%)
] 515431
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 515431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33696102
63.8%
Common 19094462
36.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4062648
 
12.1%
i 3221374
 
9.6%
o 2785686
 
8.3%
t 2605233
 
7.7%
r 2079818
 
6.2%
u 1791582
 
5.3%
m 1538482
 
4.6%
l 1500151
 
4.5%
d 1480737
 
4.4%
a 1309899
 
3.9%
Other values (42) 11320492
33.6%
Common
ValueCountFrequency (%)
10953844
57.4%
' 4710412
24.7%
, 1839775
 
9.6%
] 515431
 
2.7%
[ 515431
 
2.7%
1 208250
 
1.1%
2 159142
 
0.8%
3 98358
 
0.5%
4 48625
 
0.3%
5 21373
 
0.1%
Other values (5) 23821
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52790564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10953844
20.7%
' 4710412
 
8.9%
e 4062648
 
7.7%
i 3221374
 
6.1%
o 2785686
 
5.3%
t 2605233
 
4.9%
r 2079818
 
3.9%
, 1839775
 
3.5%
u 1791582
 
3.4%
m 1538482
 
2.9%
Other values (57) 17201710
32.6%
Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:30.569384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9839571
Min length6

Characters and Unicode

Total characters3599748
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13 days
2nd row234 day
3rd row616 day
4th row656 day
5th row444 day
ValueCountFrequency (%)
day 439723
42.7%
days 75708
 
7.3%
1 2584
 
0.3%
322 2304
 
0.2%
120 2284
 
0.2%
338 1959
 
0.2%
534 1939
 
0.2%
394 1904
 
0.2%
429 1859
 
0.2%
241 1801
 
0.2%
Other values (723) 498797
48.4%
2023-11-09T21:29:31.124313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
515431
14.3%
d 515431
14.3%
a 515431
14.3%
y 515431
14.3%
3 185648
 
5.2%
1 177429
 
4.9%
2 177273
 
4.9%
4 170907
 
4.7%
6 163439
 
4.5%
5 159006
 
4.4%
Other values (5) 504322
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1622001
45.1%
Decimal Number 1462316
40.6%
Space Separator 515431
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 185648
12.7%
1 177429
12.1%
2 177273
12.1%
4 170907
11.7%
6 163439
11.2%
5 159006
10.9%
7 122759
8.4%
0 103831
7.1%
8 101430
6.9%
9 100594
6.9%
Lowercase Letter
ValueCountFrequency (%)
d 515431
31.8%
a 515431
31.8%
y 515431
31.8%
s 75708
 
4.7%
Space Separator
ValueCountFrequency (%)
515431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1977747
54.9%
Latin 1622001
45.1%

Most frequent character per script

Common
ValueCountFrequency (%)
515431
26.1%
3 185648
 
9.4%
1 177429
 
9.0%
2 177273
 
9.0%
4 170907
 
8.6%
6 163439
 
8.3%
5 159006
 
8.0%
7 122759
 
6.2%
0 103831
 
5.2%
8 101430
 
5.1%
Latin
ValueCountFrequency (%)
d 515431
31.8%
a 515431
31.8%
y 515431
31.8%
s 75708
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3599748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
515431
14.3%
d 515431
14.3%
a 515431
14.3%
y 515431
14.3%
3 185648
 
5.2%
1 177429
 
4.9%
2 177273
 
4.9%
4 170907
 
4.7%
6 163439
 
4.5%
5 159006
 
4.4%
Other values (5) 504322
14.0%

lat
Real number (ℝ)

Distinct1472
Distinct (%)0.3%
Missing3268
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean49.442788
Minimum41.328376
Maximum52.400181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:31.327943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.328376
5-th percentile41.386146
Q148.214277
median51.499981
Q351.516288
95-th percentile52.36813
Maximum52.400181
Range11.071805
Interquartile range (IQR)3.3020117

Descriptive statistics

Standard deviation3.4673227
Coefficient of variation (CV)0.070127977
Kurtosis0.65298874
Mean49.442788
Median Absolute Deviation (MAD)0.0577152
Skewness-1.4035785
Sum25322766
Variance12.022327
MonotonicityNot monotonic
2023-11-09T21:29:31.531799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.5019097 4789
 
0.9%
51.5110993 4256
 
0.8%
51.5009609 4169
 
0.8%
51.499046 3578
 
0.7%
51.5108412 3212
 
0.6%
51.5109945 2958
 
0.6%
51.499981 2768
 
0.5%
51.5195688 2628
 
0.5%
51.4935083 2565
 
0.5%
51.5024348 2551
 
0.5%
Other values (1462) 478689
92.9%
(Missing) 3268
 
0.6%
ValueCountFrequency (%)
41.3283758 572
0.1%
41.368437 575
0.1%
41.3703041 229
 
< 0.1%
41.371308 1082
0.2%
41.3725246 120
 
< 0.1%
41.3727844 265
 
0.1%
41.3732462 797
0.2%
41.3747031 179
 
< 0.1%
41.3747873 158
 
< 0.1%
41.3750293 932
0.2%
ValueCountFrequency (%)
52.4001813 312
 
0.1%
52.3924898 467
 
0.1%
52.3923684 143
 
< 0.1%
52.3872884 856
0.2%
52.3856494 1071
0.2%
52.385601 1686
0.3%
52.3846059 916
0.2%
52.3840358 108
 
< 0.1%
52.3793659 845
0.2%
52.3786823 594
 
0.1%

lng
Real number (ℝ)

Distinct1472
Distinct (%)0.3%
Missing3268
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.8240571
Minimum-0.3697581
Maximum16.429233
Zeros0
Zeros (%)0.0%
Negative256225
Negative (%)49.7%
Memory size3.9 MiB
2023-11-09T21:29:31.744013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3697581
5-th percentile-0.1947475
Q1-0.143372
median-0.0002497
Q34.834443
95-th percentile16.356445
Maximum16.429233
Range16.798991
Interquartile range (IQR)4.977815

Descriptive statistics

Standard deviation4.58073
Coefficient of variation (CV)1.6220388
Kurtosis2.7754465
Mean2.8240571
Median Absolute Deviation (MAD)0.2832766
Skewness1.8957167
Sum1446377.6
Variance20.983088
MonotonicityNot monotonic
2023-11-09T21:29:31.944964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0232208 4789
 
0.9%
-0.1208673 4256
 
0.8%
-0.1165913 4169
 
0.8%
-0.1917073 3578
 
0.7%
-0.0780581 3212
 
0.6%
-0.1863417 2958
 
0.6%
-0.1928791 2768
 
0.5%
-0.170521 2628
 
0.5%
-0.1834346 2565
 
0.5%
-0.0002497 2551
 
0.5%
Other values (1462) 478689
92.9%
(Missing) 3268
 
0.6%
ValueCountFrequency (%)
-0.3697581 413
 
0.1%
-0.3192925 391
 
0.1%
-0.306071 128
 
< 0.1%
-0.2915052 385
 
0.1%
-0.290706 680
 
0.1%
-0.2864945 1212
0.2%
-0.284704 1848
0.4%
-0.2835263 2227
0.4%
-0.282992 197
 
< 0.1%
-0.2787261 1251
0.2%
ValueCountFrequency (%)
16.4292329 41
 
< 0.1%
16.4219737 224
< 0.1%
16.4217627 426
0.1%
16.4210093 361
0.1%
16.4200957 431
0.1%
16.417026 143
 
< 0.1%
16.4133973 191
 
< 0.1%
16.4129493 501
0.1%
16.4116997 92
 
< 0.1%
16.4082294 63
 
< 0.1%

sample
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1
386496 
0
128935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters515431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

Length

2023-11-09T21:29:32.125475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T21:29:32.262625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

Most occurring characters

ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 515431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 515431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 515431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 386496
75.0%
0 128935
 
25.0%

reviewer_score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2967154
Minimum0
Maximum10
Zeros128935
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-11-09T21:29:32.408967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.9
Q39.6
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation3.9029668
Coefficient of variation (CV)0.61984171
Kurtosis-1.0635127
Mean6.2967154
Median Absolute Deviation (MAD)1.7
Skewness-0.78685586
Sum3245522.3
Variance15.23315
MonotonicityNot monotonic
2023-11-09T21:29:32.592489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 128935
25.0%
10 86749
16.8%
9.6 53471
10.4%
9.2 44020
 
8.5%
8.8 34767
 
6.7%
8.3 30881
 
6.0%
7.5 26136
 
5.1%
7.9 24881
 
4.8%
7.1 18518
 
3.6%
6.7 14098
 
2.7%
Other values (28) 52975
10.3%
ValueCountFrequency (%)
0 128935
25.0%
2.5 1628
 
0.3%
2.9 1207
 
0.2%
3 25
 
< 0.1%
3.1 6
 
< 0.1%
3.3 2058
 
0.4%
3.5 61
 
< 0.1%
3.8 3012
 
0.6%
4 66
 
< 0.1%
4.2 3822
 
0.7%
ValueCountFrequency (%)
10 86749
16.8%
9.6 53471
10.4%
9.5 523
 
0.1%
9.4 47
 
< 0.1%
9.2 44020
8.5%
9 483
 
0.1%
8.8 34767
6.7%
8.5 379
 
0.1%
8.3 30881
 
6.0%
8.1 28
 
< 0.1%

Interactions

2023-11-09T21:29:16.611917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:00.514129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:02.480937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:04.380743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:06.399530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:08.587700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:10.556117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:12.522323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:14.474106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:16.829287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:00.777142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:02.713356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:04.597766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:06.599817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:08.787665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:10.797416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:12.755710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:14.674523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:17.054270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:00.999695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:02.902229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:04.825571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:06.799783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:09.005036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:11.004183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:12.939410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:15.012768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:17.277061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:01.218139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:03.112727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:05.050025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:07.049972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:09.219073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:11.237637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:13.189463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:15.325288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:17.494045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:01.433563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:03.312929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:05.269958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:07.272660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:09.449940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:11.456348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:13.425785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:15.546926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:17.694177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:01.649799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:03.522945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:05.517320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:07.517125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:09.652875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:11.671727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:13.650014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:15.761799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:17.894210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:01.867132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:03.730339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:05.732355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:07.969544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:09.918853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:11.888438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:13.856396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:15.975654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:18.111125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:02.064713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:03.930236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:05.982563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:08.184582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:10.138498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:12.088513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:14.073690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:16.192546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:18.294520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:02.281586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:04.180423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:06.182875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:08.384699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:10.356633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:12.305341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:14.274014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-09T21:29:16.393038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-09T21:29:32.709451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
additional_number_of_scoringaverage_scorereview_total_negative_word_countstotal_number_of_reviewsreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_givenlatlngreviewer_scoresample
additional_number_of_scoring1.000-0.1280.0490.859-0.057-0.1050.425-0.385-0.0270.000
average_score-0.1281.000-0.159-0.1930.1390.041-0.0860.1800.2000.000
review_total_negative_word_counts0.049-0.1591.0000.0520.0230.0080.036-0.050-0.2650.000
total_number_of_reviews0.859-0.1930.0521.000-0.040-0.0390.151-0.044-0.0420.000
review_total_positive_word_counts-0.0570.1390.023-0.0401.0000.047-0.0250.0600.1770.000
total_number_of_reviews_reviewer_has_given-0.1050.0410.008-0.0390.0471.000-0.1000.117-0.0140.003
lat0.425-0.0860.0360.151-0.025-0.1001.000-0.324-0.0150.000
lng-0.3850.180-0.050-0.0440.0600.117-0.3241.0000.0350.000
reviewer_score-0.0270.200-0.265-0.0420.177-0.014-0.0150.0351.0001.000
sample0.0000.0000.0000.0000.0000.0030.0000.0001.0001.000

Missing values

2023-11-09T21:29:18.644979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-09T21:29:19.512470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-09T21:29:20.680283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score
0Via Senigallia 6 20161 Milan Italy9047/21/20178.1Hotel Da VinciUnited KingdomWould have appreciated a shop in the hotel that sold drinking water etc but not necessity Would recommend if like us you arrive late at night to bring drinks from plane airport as there s no shop nearby There is a minibar though if you want to pay those prices5216670Hotel was great clean friendly staff free breakfast every morning with good selection good wifi connection nice sized room with bath fridge in room Personally loved the fact that the hotel isn t in the city centre but is literally next to a train station that you can easily get to and from the airport city Would definitely stay again621[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ']13 days45.5331379.17110200.0
1Arlandaweg 10 Westpoort 1043 EW Amsterdam Netherlands61212/12/20168.6Urban Lodge HotelBelgiumNo tissue paper box was present at the room105018No Positive07[' Leisure trip ', ' Group ', ' Triple Room ', ' Stayed 1 night ']234 day52.3856494.83444300.0
2Mallorca 251 Eixample 08008 Barcelona Spain4611/26/20158.3Alexandra Barcelona A DoubleTree by HiltonSwedenPillows3351Nice welcoming and service515[' Business trip ', ' Solo traveler ', ' Twin Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']616 day41.3931922.16152000.0
3Piazza Della Repubblica 17 Central Station 20124 Milan Italy24110/17/20159.1Hotel Principe Di SavoiaUnited States of AmericaNo Negative01543Everything including the nice upgrade The Hotel has been revamped and what a surprise Love every second of it including in room dining which was excellent279[' Leisure trip ', ' Couple ', ' Ambassador Junior Suite ', ' Stayed 1 night ']656 day45.4798889.19629800.0
4Singel 303 309 Amsterdam City Center 1012 WJ Amsterdam Netherlands8345/16/20169.1Hotel Esther aUnited KingdomNo Negative04687Lovely hotel v welcoming staff72[' Business trip ', ' Solo traveler ', ' Classic Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']444 day52.3705454.88864400.0
5Coram Street Camden London WC1N 1HT United Kingdom7098/13/20158.2Holiday Inn London BloomsburyEcuadorThey don t have free wifi72995The location is perfect if you don t have a lot of time and you want to have a look at the city centre263[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 1 night ']721 day51.524125-0.12580700.0
6Empire Way Wembley Brent London HA9 8DS United Kingdom10058/18/20168.3Holiday Inn London WembleyUnited KingdomRoom generally a bit shabby with some lack of maintenance Some crumbs on bedroom floor these issues did not spoil our minibreak It would be nice to have vegetarian sausages available for breakfast353469Location price It did not cost much more to have breakfast included Room was a reasonable size and bed was comfortable2311[' Leisure trip ', ' Couple ', ' Queen Room ', ' Stayed 1 night ']350 day51.559095-0.28470400.0
71 Shortlands Hammersmith and Fulham London W6 8DR United Kingdom7048/11/20158.3Novotel London WestNetherlandsExecutive rooms 9th Floor don t have a bath Their website made it look like all rooms did have one and when being at the end of a hall there s no wifi connection possible Mind that during my first two stays here I did have a perfect wifi connection522443Comphy bed upgraded to executive room with nespresso machine etc for only 24 3 nights quiet room clean 4 free waters in the fridge tho no refill and close to Hammersmith station shops and Starbucks Olympia is in walking distance too4238[' Business trip ', ' Solo traveler ', ' Executive Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']723 day51.491959-0.22009600.0
835 Rue Caumartin 9th arr 75009 Paris France2116/25/20168.9Hotel Saint Petersbourg OperaIrelandPity about the two days of rain82412Its centrality proximity to our destination71[' Group ', ' Double or Twin Room ', ' Stayed 1 night ']404 day48.8721742.32807500.0
949 Gloucester Place Marble Arch Westminster Borough London W1U 8JE United Kingdom619/30/20157.4St George HotelCanadaDidn t like it at all construction was in progress stuff lied to us about vacancy18334Didn t like anything about the stay if i had a chance to change or cancel it I would do it right away251[' Couple ', ' Standard Triple Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']673 day51.518277-0.15835100.0
hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score
5154213 rue de Ponthieu 8th arr 75008 Paris France7012/1/20168.7H tel Mathis Elys esUnited States of AmericaNo Negative0652Location25[' Leisure trip ', ' Group ', ' Junior Suite ', ' Stayed 2 nights ', ' Submitted from a mobile device ']245 day48.8700332.31127419.6
51542215 Rue Boissy d Anglas 8th arr 75008 Paris France9112/21/20168.5Sofitel Paris Le FaubourgUnited Arab EmiratesNo Negative0564Location was perfect Room was very comfortable spacious1040[' Leisure trip ', ' Solo traveler ', ' Luxury Room 1 Queensize Bed Twin bedded Room On Request ', ' Stayed 6 nights ', ' Submitted from a mobile device ']225 day48.8684142.32132519.2
51542352 56 Inverness Terrace Westminster Borough London W2 3LB United Kingdom54510/12/20158.0Shaftesbury Hyde Park InternationalUnited Kingdomstaff miserable and room very small72907Outstanding location31[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ']661 day51.512397-0.18612415.8
51542422 Portman Square Westminster Borough London W1H 7BG United Kingdom5978/16/20167.9Radisson Blu Portman Hotel LondonUnited KingdomRoom was very small so much so that I kept hitting myself on the TV that was mounted on the wall Bed was very soft and pillows were awful302308Staff were friendly and efficient63[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']352 day51.516191-0.15794915.0
51542524 Ludgate Hill City of London London EC4M 7DR United Kingdom91811/8/20158.4Club Quarters Hotel St Paul sSwedenOur room was really cold and we had problem with the heater They had to bring a portable heater to fix the issue254117It is a nice and clean hotel with a good location133[' Leisure trip ', ' Group ', ' Standard Queen Room ', ' Stayed 3 nights ']634 day51.513930-0.10112617.9
5154269 Knaresborough Place Kensington and Chelsea London SW5 0TP United Kingdom1074/19/20179.0Hotel MoonlightFranceNo Negative0617Tr s proche du metro Earl s court1010[' Leisure trip ', ' Group ', ' Club Double or Twin Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']106 day51.494028-0.19105018.8
515427Landstra er Hauptstra e 155 03 Landstra e 1030 Vienna Austria2722/13/20178.4BEST WESTERN PLUS Amedia WienTurkeyNo Negative03224The bed was so comfy I stayed with my boyfriend we had a double bed Also transportation is excellent the hotel is very very close to Old City Once you exit the hotel just turn right about 50m away there is a bus stop get off on Stubentor it is the last stop It only takes 10min Also you can take the same bus back to the hotel The bus name is 74A St Marx The hotel was very clean and the room that we accomidated in was nice and roomy931[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 4 nights ', ' Submitted from a mobile device ']171 day48.19237916.39945119.2
51542829 31 Gower Street Camden London WC1E 6HG United Kingdom4572/7/20166.8Bloomsbury Palace HotelNetherlandsroom is really small but guess is normal in London122751great location simple check in out nice shower921[' Business trip ', ' Solo traveler ', ' Single Room ', ' Stayed 1 night ']543 day51.520795-0.13108418.3
51542931 Great Cumberland Place Westminster Borough London W1H 7TA United Kingdom3655/21/20178.1The Marble Arch LondonUnited Arab EmiratesNo Negative01567Location and very comfy bed628[' Leisure trip ', ' Solo traveler ', ' Deluxe Double Room ', ' Stayed 2 nights ']74 days51.515125-0.16006619.2
51543025 Courtfield Gardens Kensington and Chelsea London SW5 0PG United Kingdom2228/5/20169.0The Nadler KensingtonAustraliaPatio outside could have been cleaned of algae to give a more uplifting atmosphere to a downstairs room201209Beds comfortable Pillows also good Homely feel although room was small Staff very pleasant and helpful thank you202[' Leisure trip ', ' Couple ', ' Bunk Bed Room ', ' Stayed 4 nights ']363 day51.493109-0.19020818.8

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Most frequently occurring

hotel_addressadditional_number_of_scoringreview_dateaverage_scorehotel_namereviewer_nationalitynegative_reviewreview_total_negative_word_countstotal_number_of_reviewspositive_reviewreview_total_positive_word_countstotal_number_of_reviews_reviewer_has_giventagsdays_since_reviewlatlngsamplereviewer_score# duplicates
0167 rue de Rome 17th arr 75017 Paris France1112/12/20166.8Villa EugenieCanadaListed above3165It was a terrible stat unfriendly staff very unprofessional and dirty rooms131[' Business trip ', ' Solo traveler ', ' Standard Double or Twin Room ', ' Stayed 6 nights ', ' Submitted from a mobile device ']234 day48.8871282.31420500.02
140 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France22810/3/20157.9H tel Concorde MontparnasseChinaThe restroom can be improved62515The location is really convenience very close to the SNCF and metro Staff is professional and friendly My cousin met electricity power off situation the staff solved the problem in 5 mins343[' Leisure trip ', ' Group ', ' Classic Twin Room ', ' Stayed 5 nights ', ' Submitted from a mobile device ']670 day48.8381082.31866900.02
240 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France22810/6/20157.9H tel Concorde MontparnasseUnited KingdomThe hotel is relatively expensive food available it is a bit of a limited menu172515Location is very convenient Hotel is of a good standard rooms are very nice163[' Leisure trip ', ' Family with older children ', ' Superior Double Room ', ' Stayed 2 nights ']667 day48.8381082.31866900.02
340 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France22811/10/20167.9H tel Concorde MontparnasseIrelandTemp in the Hotel warm but room had set temp so couldn t change it172515Food excellent staff very friendly and helpful Room was large and bed comfy Had presser in room that was great for suits246[' Business trip ', ' Solo traveler ', ' Deluxe Double Room ', ' Stayed 3 nights ']266 day48.8381082.31866900.02
440 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France22811/17/20157.9H tel Concorde MontparnasseUnited KingdomNo drinking water provided in room Breakfast was cold Assistance was slow132515Cleanliness Comfort31[' Leisure trip ', ' Couple ', ' Classic Double Room ', ' Stayed 3 nights ']625 day48.8381082.31866900.02
540 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France22812/24/20157.9H tel Concorde MontparnasseFranceNo Negative02515Good location for station and buses to CDG Airport1132[' Leisure trip ', ' Couple ', ' Classic Double Room ', ' Stayed 1 night ']588 day48.8381082.31866900.02
640 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2282/1/20167.9H tel Concorde MontparnasseMaltabeautiful wiew32515cleanness staff very friendly very helpful72[' Leisure trip ', ' Couple ', ' Classic Double Room ', ' Stayed 3 nights ']549 day48.8381082.31866900.02
740 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2282/27/20177.9H tel Concorde MontparnasseUnited Kingdombathroom could have been cleaner was a bit too far out from major attractions152515bed was very comfortable and clean71[' Leisure trip ', ' Family with young children ', ' Classic Twin Room ', ' Stayed 3 nights ']157 day48.8381082.31866900.02
840 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2283/27/20167.9H tel Concorde MontparnasseUnited States of AmericaNo Negative02515clean spacious non smoking room in a good location room was quiet staff was very courteous179[' Leisure trip ', ' Solo traveler ', ' Classic Twin Room ', ' Stayed 4 nights ']494 day48.8381082.31866900.02
940 Rue du Commandant Ren Mouchotte 14th arr 75014 Paris France2283/29/20167.9H tel Concorde MontparnasseGermanyRefrigerator22515Tea machine and hair dryer68[' Leisure trip ', ' Couple ', ' Classic Double Room ', ' Stayed 4 nights ', ' Submitted from a mobile device ']492 day48.8381082.31866900.02